



import os
import re
import warnings
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
				from .tokenization_ta import TaTokenizer
else:
				lowerCAmelCase__	    =    None


lowerCAmelCase__	    =    logging.get_logger(__name__)

lowerCAmelCase__	    =    {"""vocab_file""": """spiece.model""", """tokenizer_file""": """tokenizer.json"""}

lowerCAmelCase__	    =    {
    """vocab_file""": {
        """t5-small""": """https://huggingface.co/t5-small/resolve/main/spiece.model""",
        """t5-base""": """https://huggingface.co/t5-base/resolve/main/spiece.model""",
        """t5-large""": """https://huggingface.co/t5-large/resolve/main/spiece.model""",
        """t5-3b""": """https://huggingface.co/t5-3b/resolve/main/spiece.model""",
        """t5-11b""": """https://huggingface.co/t5-11b/resolve/main/spiece.model""",
    },
    """tokenizer_file""": {
        """t5-small""": """https://huggingface.co/t5-small/resolve/main/tokenizer.json""",
        """t5-base""": """https://huggingface.co/t5-base/resolve/main/tokenizer.json""",
        """t5-large""": """https://huggingface.co/t5-large/resolve/main/tokenizer.json""",
        """t5-3b""": """https://huggingface.co/t5-3b/resolve/main/tokenizer.json""",
        """t5-11b""": """https://huggingface.co/t5-11b/resolve/main/tokenizer.json""",
    },
}


# TODO(PVP) - this should be removed in Transformers v5
lowerCAmelCase__	    =    {
    """t5-small""": 5_1_2,
    """t5-base""": 5_1_2,
    """t5-large""": 5_1_2,
    """t5-3b""": 5_1_2,
    """t5-11b""": 5_1_2,
}

class  snake_case__(_UpperCAmelCase     ):

					"""simple docstring"""






					lowercase_								=						VOCAB_FILES_NAMES
					lowercase_								=						PRETRAINED_VOCAB_FILES_MAP
					lowercase_								=						PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					lowercase_								=						['''input_ids''', '''attention_mask''']
					lowercase_								=						TaTokenizer

					lowercase_								=						[]







					def __init__(				self   :      Any ,  SCREAMING_SNAKE_CASE   :      str=None ,  SCREAMING_SNAKE_CASE   :      Optional[Any]=None ,  SCREAMING_SNAKE_CASE   :      List[str]="</s>" ,  SCREAMING_SNAKE_CASE   :      Dict="<unk>" ,  SCREAMING_SNAKE_CASE   :      str="<pad>" ,  SCREAMING_SNAKE_CASE   :      Optional[int]=100 ,  SCREAMING_SNAKE_CASE   :      List[str]=None ,  **SCREAMING_SNAKE_CASE   :      Dict ,  ):


									if extra_ids > 0 and additional_special_tokens is None:
													lowercase__	:       Dict						=	[f"""<extra_id_{i}>""" for i in range(lowerCAmelCase__	)]
									elif extra_ids > 0 and additional_special_tokens is not None:
													# Check that we have the right number of extra special tokens
													lowercase__	:       Optional[Any]						=	len(set(filter(lambda SCREAMING_SNAKE_CASE	: bool("extra_id_" in str(lowerCAmelCase__	)	) ,  lowerCAmelCase__	)	)	)
													if extra_tokens != extra_ids:
																	raise ValueError(
																	    f"""Both extra_ids ({extra_ids}) and additional_special_tokens ({additional_special_tokens}) are"""
																	    " provided to T5Tokenizer. In this case the additional_special_tokens must include the extra_ids"
																	    " tokens"	)

									super().__init__(
									    lowerCAmelCase__ ,  tokenizer_file=lowerCAmelCase__ ,  eos_token=lowerCAmelCase__ ,  unk_token=lowerCAmelCase__ ,  pad_token=lowerCAmelCase__ ,  extra_ids=lowerCAmelCase__ ,  additional_special_tokens=lowerCAmelCase__ ,  **lowerCAmelCase__ ,  )

									lowercase__	:       Optional[Any]						=	vocab_file
									lowercase__	:       List[str]						=	False if not self.vocab_file else True
									lowercase__	:       Tuple						=	extra_ids







					@staticmethod
					def 					snake_case	(				SCREAMING_SNAKE_CASE   :      Union[str, Any] ,  SCREAMING_SNAKE_CASE   :      str ,  SCREAMING_SNAKE_CASE   :      Optional[Any]	):


									if pretrained_model_name_or_path in TaTokenizerFast.max_model_input_sizes:
													lowercase__	:       Any						=	TaTokenizerFast.max_model_input_sizes[pretrained_model_name_or_path]
													if init_max_model_length is not None and init_max_model_length != max_model_length:
																	return init_max_model_length
													elif init_max_model_length is None:
																	warnings.warn(
																	    "This tokenizer was incorrectly instantiated with a model max length of"
																	    f""" {deprecated_max_model_length} which will be corrected in Transformers v5.\nFor now, this"""
																	    " behavior is kept to avoid breaking backwards compatibility when padding/encoding with"
																	    " `truncation is True`.\n- Be aware that you SHOULD NOT rely on"
																	    f""" {pretrained_model_name_or_path} automatically truncating your input to"""
																	    f""" {deprecated_max_model_length} when padding/encoding.\n- If you want to encode/pad to sequences"""
																	    f""" longer than {deprecated_max_model_length} you can either instantiate this tokenizer with"""
																	    " `model_max_length` or pass `max_length` when encoding/padding.\n- To avoid this warning, please"
																	    " instantiate this tokenizer with `model_max_length` set to your preferred value." ,  lowerCAmelCase__ ,  )

									return max_model_length







					def 					snake_case	(				self   :      Union[str, Any] ,  SCREAMING_SNAKE_CASE   :      List[str] ,  SCREAMING_SNAKE_CASE   :      Tuple = None	):


									if not self.can_save_slow_tokenizer:
													raise ValueError(
													    "Your fast tokenizer does not have the necessary information to save the vocabulary for a slow "
													    "tokenizer."	)

									if not os.path.isdir(lowerCAmelCase__	):
													logger.error(f"""Vocabulary path ({save_directory}) should be a directory"""	)
													return
									lowercase__	:       int						=	os.path.join(
									    lowerCAmelCase__ ,  (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]	)

									if os.path.abspath(self.vocab_file	) != os.path.abspath(lowerCAmelCase__	):
													copyfile(self.vocab_file ,  lowerCAmelCase__	)
													logger.info(f"""Copy vocab file to {out_vocab_file}"""	)

									return (out_vocab_file,)







					def 					snake_case	(				self   :      List[str] ,  SCREAMING_SNAKE_CASE   :      int ,  SCREAMING_SNAKE_CASE   :      List[Any] = None	):
									lowercase__	:       Optional[Any]						=	token_ids_a + [self.eos_token_id]
									if token_ids_a is None:
													return self.prefix_tokens + token_ids_a
									else:
													lowercase__	:       Optional[Any]						=	token_ids_a + [self.eos_token_id]
													return self.prefix_tokens + token_ids_a + token_ids_a







					def 					snake_case	(				self   :      List[Any] ,  SCREAMING_SNAKE_CASE   :      Optional[int] ,  SCREAMING_SNAKE_CASE   :      Dict = None	):
									lowercase__	:       str						=	[self.eos_token_id]

									if token_ids_a is None:
													return len(token_ids_a + eos	) * [0]
									return len(token_ids_a + eos + token_ids_a + eos	) * [0]







					def 					snake_case	(				self   :      str	):
									return list(
									    set(filter(lambda SCREAMING_SNAKE_CASE	: bool(re.search(r"<extra_id_\d+>" ,  lowerCAmelCase__	)	) is not None ,  self.additional_special_tokens	)	)	)







					def 					snake_case	(				self   :      Dict	):
									return [self.convert_tokens_to_ids(lowerCAmelCase__	) for token in self.get_sentinel_tokens()]





import argparse
import os

import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed

from accelerate import Accelerator, DistributedType


########################################################################
# This is a fully working simple example to use Accelerate,
# specifically showcasing how to properly calculate the metrics on the
# validation dataset when in a distributed system, and builds off the
# `nlp_example.py` script.
#
# This example trains a Bert base model on GLUE MRPC
# in any of the following settings (with the same script):
#   - single CPU or single GPU
#   - multi GPUS (using PyTorch distributed mode)
#   - (multi) TPUs
#   - fp16 (mixed-precision) or fp32 (normal precision)
#
# To help focus on the differences in the code, building `DataLoaders`
# was refactored into its own function.
# New additions from the base script can be found quickly by
# looking for the # New Code # tags
#
# To run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################


__a    :	Union[str, Any]         =    1_6
__a    :	int         =    3_2





def 							UpperCAmelCase		(       lowercase				,     lowercase = 16				):







				"""simple docstring"""



				__lowercase					     =				AutoTokenizer.from_pretrained('''bert-base-cased'''				)
				__lowercase					     =				load_dataset('''glue'''				,     '''mrpc'''				)

				def tokenize_function(lowercase				):
								# max_length=None => use the model max length (it's actually the default)
								__lowercase					     =				tokenizer(examples['''sentence1''']				,     examples['''sentence2''']				,     truncation=lowercase				,     max_length=lowercase				)
								return outputs

				# Apply the method we just defined to all the examples in all the splits of the dataset
				# starting with the main process first:
				with accelerator.main_process_first():
								__lowercase					     =				datasets.map(
								    lowercase				,     batched=lowercase				,     remove_columns=['''idx''', '''sentence1''', '''sentence2''']				,     )

				# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
				# transformers library
				__lowercase					     =				tokenized_datasets.rename_column('''label'''				,     '''labels'''				)

				def collate_fn(lowercase				):
								# On TPU it's best to pad everything to the same length or training will be very slow.
								__lowercase					     =				128 if accelerator.distributed_type == DistributedType.TPU else None
								# When using mixed precision we want round multiples of 8/16
								if accelerator.mixed_precision == "fp8":
												__lowercase					     =				16
								elif accelerator.mixed_precision != "no":
												__lowercase					     =				8
								else:
												__lowercase					     =				None

								return tokenizer.pad(
								    lowercase				,     padding='''longest'''				,     max_length=lowercase				,     pad_to_multiple_of=lowercase				,     return_tensors='''pt'''				,     )

				# Instantiate dataloaders.
				__lowercase					     =				DataLoader(
				    tokenized_datasets['''train''']				,     shuffle=lowercase				,     collate_fn=lowercase				,     batch_size=lowercase				)
				__lowercase					     =				DataLoader(
				    tokenized_datasets['''validation''']				,     shuffle=lowercase				,     collate_fn=lowercase				,     batch_size=lowercase				)

				return train_dataloader, eval_dataloader


# For testing only
if os.environ.get("""TESTING_MOCKED_DATALOADERS""", None) == "1":
		from accelerate.test_utils.training import mocked_dataloaders

		__a    :	List[Any]         =    mocked_dataloaders  # noqa: F811





def 							UpperCAmelCase		(       lowercase				,     lowercase				):







				"""simple docstring"""



				if os.environ.get('''TESTING_MOCKED_DATALOADERS'''				,     lowercase				) == "1":
								__lowercase					     =				2
				# Initialize accelerator
				__lowercase					     =				Accelerator(cpu=args.cpu				,     mixed_precision=args.mixed_precision				)
				# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
				__lowercase					     =				config['''lr''']
				__lowercase					     =				int(config['''num_epochs''']				)
				__lowercase					     =				int(config['''seed''']				)
				__lowercase					     =				int(config['''batch_size''']				)

				__lowercase					     =				evaluate.load('''glue'''				,     '''mrpc'''				)

				# If the batch size is too big we use gradient accumulation
				__lowercase					     =				1
				if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
								__lowercase					     =				batch_size // MAX_GPU_BATCH_SIZE
								__lowercase					     =				MAX_GPU_BATCH_SIZE

				set_seed(lowercase				)
				__lowercase     ,	__lowercase					     =				get_dataloaders(lowercase				,     lowercase				)
				# Instantiate the model (we build the model here so that the seed also control new weights initialization)
				__lowercase					     =				AutoModelForSequenceClassification.from_pretrained('''bert-base-cased'''				,     return_dict=lowercase				)

				# We could avoid this line since the accelerator is set with `device_placement=True` (default value).
				# Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
				# creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
				__lowercase					     =				model.to(accelerator.device				)

				# Instantiate optimizer
				__lowercase					     =				AdamW(params=model.parameters()				,     lr=lowercase				)

				# Instantiate scheduler
				__lowercase					     =				get_linear_schedule_with_warmup(
				    optimizer=lowercase				,     num_warmup_steps=100				,     num_training_steps=(len(lowercase				) * num_epochs) // gradient_accumulation_steps				,     )

				# Prepare everything
				# There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
				# prepare method.
				__lowercase     ,	__lowercase     ,	__lowercase     ,	__lowercase     ,	__lowercase					     =				accelerator.prepare(
				    lowercase				,     lowercase				,     lowercase				,     lowercase				,     lowercase				)

				# Now we train the model
				for epoch in range(lowercase				):
								model.train()
								for step, batch in enumerate(lowercase				):
												# We could avoid this line since we set the accelerator with `device_placement=True`.
												batch.to(accelerator.device				)
												__lowercase					     =				model(**lowercase				)
												__lowercase					     =				outputs.loss
												__lowercase					     =				loss / gradient_accumulation_steps
												accelerator.backward(lowercase				)
												if step % gradient_accumulation_steps == 0:
																optimizer.step()
																lr_scheduler.step()
																optimizer.zero_grad()

								model.eval()
								__lowercase					     =				0
								for step, batch in enumerate(lowercase				):
												# We could avoid this line since we set the accelerator with `device_placement=True`.
												batch.to(accelerator.device				)
												with torch.no_grad():
																__lowercase					     =				model(**lowercase				)
												__lowercase					     =				outputs.logits.argmax(dim=-1				)
												__lowercase     ,	__lowercase					     =				accelerator.gather((predictions, batch['''labels'''])				)
												# New Code #
												# First we check if it's a distributed system
												if accelerator.use_distributed:
																# Then see if we're on the last batch of our eval dataloader
																if step == len(lowercase				) - 1:
																				# Last batch needs to be truncated on distributed systems as it contains additional samples
																				__lowercase					     =				predictions[: len(eval_dataloader.dataset				) - samples_seen]
																				__lowercase					     =				references[: len(eval_dataloader.dataset				) - samples_seen]
																else:
																				# Otherwise we add the number of samples seen
																				samples_seen += references.shape[0]
            # All of this can be avoided if you use `Accelerator.gather_for_metrics` instead of `Accelerator.gather`:
            # accelerator.gather_for_metrics((predictions, batch["labels"]))
												metric.add_batch(
												    predictions=lowercase				,     references=lowercase				,     )

								__lowercase					     =				metric.compute()
								# Use accelerator.print to print only on the main process.
								accelerator.print(F"epoch {epoch}:"				,     lowercase				)





def 							UpperCAmelCase		(       ):







				"""simple docstring"""



				__lowercase					     =				argparse.ArgumentParser(description='''Simple example of training script.'''				)
				parser.add_argument(
				    '''--mixed_precision'''				,     type=lowercase				,     default=lowercase				,     choices=['''no''', '''fp16''', '''bf16''', '''fp8''']				,     help='''Whether to use mixed precision. Choose'''
				    '''between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10.'''
				    '''and an Nvidia Ampere GPU.'''				,     )
				parser.add_argument('''--cpu'''				,     action='''store_true'''				,     help='''If passed, will train on the CPU.'''				)
				__lowercase					     =				parser.parse_args()
				__lowercase					     =				{'''lr''': 2E-5, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 16}
				training_function(lowercase				,     lowercase				)


if __name__ == "__main__":
		main()